class_docstr: Combines LSTM with a transformer layer to predict the quantiles of all future target values [Lim2021]_.
  
  Based on `gluonts.torch.model.tft.TemporalFusionTransformerEstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.tft.html>`_.
  See GluonTS documentation for additional hyperparameters.
  
  
  References
  ----------
  .. [Lim2021] Lim, Bryan, et al.
      "Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting."
      International Journal of Forecasting. 2021.
  
  
  Other Parameters
  ----------------
  context_length : int, default = max(64, 2 * prediction_length)
      Number of past values used for prediction.
  disable_static_features : bool, default = False
      If True, static features won't be used by the model even if they are present in the dataset.
      If False, static features will be used by the model if they are present in the dataset.
  disable_known_covariates : bool, default = False
      If True, known covariates won't be used by the model even if they are present in the dataset.
      If False, known covariates will be used by the model if they are present in the dataset.
  disable_past_covariates : bool, default = False
      If True, past covariates won't be used by the model even if they are present in the dataset.
      If False, past covariates will be used by the model if they are present in the dataset.
  hidden_dim : int, default = 32
      Size of the LSTM & transformer hidden states.
  variable_dim : int, default = 32
      Size of the feature embeddings.
  num_heads : int, default = 4
      Number of attention heads in self-attention layer in the decoder.
  dropout_rate : float, default = 0.1
      Dropout regularization parameter
  max_epochs : int, default = 100
      Number of epochs the model will be trained for
  batch_size : int, default = 64
      Size of batches used during training
  predict_batch_size : int, default = 500
      Size of batches used during prediction.
  num_batches_per_epoch : int, default = 50
      Number of batches processed every epoch
  lr : float, default = 1e-3,
      Learning rate used during training
  trainer_kwargs : dict, optional
      Optional keyword arguments passed to ``lightning.Trainer``.
  early_stopping_patience : int or None, default = 20
      Early stop training if the validation loss doesn't improve for this many epochs.
code: def _get_estimator_init_args(self) -> Dict[str, Any]:
      init_kwargs = super()._get_estimator_init_args()
      if self.num_feat_dynamic_real > 0:
          init_kwargs["dynamic_dims"] = [self.num_feat_dynamic_real]
      if self.num_past_feat_dynamic_real > 0:
          init_kwargs["past_dynamic_dims"] = [
              self.num_past_feat_dynamic_real
          ]
      if self.num_feat_static_real > 0:
          init_kwargs["static_dims"] = [self.num_feat_static_real]
      if len(self.feat_static_cat_cardinality):
          init_kwargs[
              "static_cardinalities"
          ] = self.feat_static_cat_cardinality
      init_kwargs.setdefault(
          "time_features", get_time_features_for_frequency(self.freq)
      )
      return init_kwargs
  
imports: import logging
  from typing import Any, Dict, Type
  from gluonts.model.estimator import Estimator
  from autogluon.timeseries.models.gluonts.abstract_gluonts import AbstractGluonTSModel
  from autogluon.timeseries.utils.datetime import get_lags_for_frequency, get_seasonality, get_time_features_for_frequency
label_desc: Use image node_id 3 for calling the TemporalFusionTransformerModel obj's underlying member method code with example usage: obj._get_estimator_init_args() and returns: init_kwargs